Dynamin-related protein-1 promotes lung cancer A549 cells apoptosis through the F-actin/bax signaling pathway.

Dynamin-related protein-1 (Drp1) has been found to be associated with cell death. The role of Drp1 in A549 cells death has not been explored. In this study, adenovirus-mediated Drp1 overexpression was used to investigate the influence of Drp1 on A549 cell viability with a focus on F-actin and Bax. Cell viability, protein expression, oxygen consumption, energy metabolism, and growth rate were measured through ELISA, qPCR, western blots and pathway analysis. Our results indicated that Drp1 overexpression promoted A549 cell death through apoptosis. Mechanistically, cytoskeletal F-actin was impaired and Bax expression was elevated in response to Drp1 overexpression. Besides, energy metabolism was reduced and oxygen consumption was interrupted. Therefore, our results demonstrated that A549 cell viability, apoptosis and growth were regulated by the Drp1/F-actin/Bax signaling pathways. These data explain a new role played by Drp1 in regulating cell viability and also provide a potential target to affect the progression of lung cancer through induction of cell death.

A powerful truncated tail strength method for testing multiple null hypotheses in one dataset.

In microarray analysis, medical imaging analysis and functional magnetic resonance imaging, we often need to test an overall null hypothesis involving a large number of single hypotheses (usually larger than 1000) in one dataset. A tail strength statistic (Taylor and Tibshirani, 2006) and Fisher's probability method are useful and can be applied to measure an overall significance for a large set of independent single hypothesis tests with the overall null hypothesis assuming that all single hypotheses are true. In this paper we propose a new method that improves the tail strength statistic by considering only the values whose corresponding p-values are less than some pre-specified cutoff. We call it truncated tail strength statistic. We illustrate our method using a simulation study and two genome-wide datasets by chromosome. Our method not only controls type one error rate quite well, but also has significantly higher power than the tail strength method and Fisher's method in most cases.

A mixed two-stage method for detecting interactions in genomewide association studies.

Genomewide association studies (GWAS) are being conducted to unravel the genetic etiology of complex diseases, in which complex epistasis may play an important role. One-stage method in which interactions are tested using all samples at one time may be computationally problematic, may have low power as the number of markers tested increases and may not be cost-efficient. A common two-stage method may be a reasonable and powerful approach for detecting interacting genes using all samples in both two stages. In this study, we introduce an alternative two-stage method, in which some promising markers are selected using a proportion of samples in the first stage and interactions are then tested using the remaining samples in the second stage. This two-stage method is called mixed two-stage method. We then investigate the power of both one-stage method and mixed two-stage method to detect interacting disease loci for a range of two-locus epistatic models in a case-control study design. Our results suggest that mixed two-stage method may be more powerful than one-stage method if we choose about 30% of samples for single-locus tests in the first stage, and identify less than and equal to 1% of markers for follow-up interaction tests. In addition, we compare both two-stage methods and find that our two-stage method will lose power because we only use part of samples in both two stages.

Two-stage designs to identify the effects of SNP combinations on complex diseases.

The genetic basis of complex diseases is expected to be highly heterogeneous, with many disease genes, where each gene by itself has only a small effect. Based on the nonlinear contributions of disease genes across the genome to complex diseases, we introduce the concept of single nucleotide polymorphism (SNP) synergistic blocks. A two-stage approach is applied to detect the genetic association of synergistic blocks with a disease. In the first stage, synergistic blocks associated with a complex disease are identified by clustering SNP patterns and choosing blocks within a cluster that minimize a diversity criterion. In the second stage, a logistic regression model is given for a synergistic block. Using simulated case-control data, we demonstrate that our method has reasonable power to identify gene-gene interactions. To further evaluate the performance of our method, we apply our method to 17 loci of four candidate genes for paranoid schizophrenia in a Chinese population. Five synergistic blocks are found to be associated with schizophrenia, three of which are negatively associated (odds ratio, OR < 0.3, P < 0.05), while the others are positively associated (OR > 2.0, P < 0.05). The mathematical models of these five synergistic blocks are presented. The results suggest that there may be interactive effects for schizophrenia among variants of the genes neuregulin 1 (NRG1, 8p22-p11), G72 (13q34), the regulator of G-protein signaling-4 (RGS4, 1q21-q22) and frizzled 3 (FZD3, 8p21). Using synergistic blocks, we can reduce the dimensionality in a multi-locus association analysis, and evaluate the sizes of interactive effects among multiple disease genes on complex phenotypes.

An entropy-based approach for testing genetic epistasis underlying complex diseases.

The genetic basis of complex diseases is expected to be highly heterogeneous, with complex interactions among multiple disease loci and environment factors. Due to the multi-dimensional property of interactions among large number of genetic loci, efficient statistical approach has not been well developed to handle the high-order epistatic complexity. In this article, we introduce a new approach for testing genetic epistasis in multiple loci using an entropy-based statistic for a case-only design. The entropy-based statistic asymptotically follows a chi(2) distribution. Computer simulations show that the entropy-based approach has better control of type I error and higher power compared to the standard chi(2) test. Motivated by a schizophrenia data set, we propose a method for measuring and testing the relative entropy of a clinical phenotype, through which one can test the contribution or interaction of multiple disease loci to a clinical phenotype. A sequential forward selection procedure is proposed to construct a genetic interaction network which is illustrated through a tree-based diagram. The network information clearly shows the relative importance of a set of genetic loci on a clinical phenotype. To show the utility of the new entropy-based approach, it is applied to analyze two real data sets, a schizophrenia data set and a published malaria data set. Our approach provides a fast and testable framework for genetic epistasis study in a case-only design.

Entropy-based joint analysis for two-stage genome-wide association studies.

Genome-wide association studies (GWAS) are being conducted to identify common genetic variants that predispose to human diseases to unravel the genetic etiology of complex human diseases now. Because of genotyping cost constraints, it often follows a two-stage design, in which a large number of markers are identified in a proportion of the available samples in stage 1, and then the markers identified in stage 1 are examined in all the samples in stage 2. In this paper, we introduce a nonlinear entropy-based statistic for joint analysis for two-stage genome-wide association studies. Type I error rates and power of the entropy-based statistic for association tests are validated using simulation studies in single-locus test. The power of entropy-based joint analysis is investigated by simulations. And the results suggest that entropy-based joint analysis is always more powerful than linear joint analysis that uses a linear function of risk allele frequencies in cases and controls when detecting rare genetic variants; the powers of these two joint analyses are comparable when detecting common genetic variants. Furthermore, when the false discovery rate is controlled, entropy-based joint analysis is more powerful and needs fewer samples than linear joint analysis that uses a linear function of risk allele frequencies in cases and controls. So, we recommend we should use entropy-based strategy for two-stage genome-wide association studies to detect the rare and common genetic variants with moderate to large genetic effect underlying a complex disease.

A combinatorial searching method for detecting a set of interacting loci associated with complex traits.

Complex diseases are presumed to be the results of the interaction of several genes and environmental factors, with each gene only having a small effect on the disease. Mapping complex disease genes therefore becomes one of the greatest challenges facing geneticists. Most current approaches of association studies essentially evaluate one marker or one gene (haplotype approach) at a time. These approaches ignore the possibility that effects of multilocus functional genetic units may play a larger role than a single-locus effect in determining trait variability. In this article, we propose a Combinatorial Searching Method (CSM) to detect a set of interacting loci (may be unlinked) that predicts the complex trait. In the application of the CSM, a simple filter is used to filter all the possible locus-sets and retain the candidate locus-sets, then a new objective function based on the cross-validation and partitions of the multi-locus genotypes is proposed to evaluate the retained locus-sets. The locus-set with the largest value of the objective function is the final locus-set and a permutation procedure is performed to evaluate the overall p-value of the test for association between the final locus-set and the trait. The performance of the method is evaluated by simulation studies as well as by being applied to a real data set. The simulation studies show that the CSM has reasonable power to detect high-order interactions. When the CSM is applied to a real data set to detect the locus-set (among the 13 loci in the ACE gene) that predicts systolic blood pressure (SBP) or diastolic blood pressure (DBP), we found that a four-locus gene-gene interaction model best predicts SBP with an overall p-value = 0.033, and similarly a two-locus gene-gene interaction model best predicts DBP with an overall p-value = 0.045.

Two-stage designs in case-control association analysis.

DNA pooling is a cost-effective approach for collecting information on marker allele frequency in genetic studies. It is often suggested as a screening tool to identify a subset of candidate markers from a very large number of markers to be followed up by more accurate and informative individual genotyping. In this article, we investigate several statistical properties and design issues related to this two-stage design, including the selection of the candidate markers for second-stage analysis, statistical power of this design, and the probability that truly disease-associated markers are ranked among the top after second-stage analysis. We have derived analytical results on the proportion of markers to be selected for second-stage analysis. For example, to detect disease-associated markers with an allele frequency difference of 0.05 between the cases and controls through an initial sample of 1000 cases and 1000 controls, our results suggest that when the measurement errors are small (0.005), approximately 3% of the markers should be selected. For the statistical power to identify disease-associated markers, we find that the measurement errors associated with DNA pooling have little effect on its power. This is in contrast to the one-stage pooling scheme where measurement errors may have large effect on statistical power. As for the probability that the disease-associated markers are ranked among the top in the second stage, we show that there is a high probability that at least one disease-associated marker is ranked among the top when the allele frequency differences between the cases and controls are not <0.05 for reasonably large sample sizes, even though the errors associated with DNA pooling in the first stage are not small. Therefore, the two-stage design with DNA pooling as a screening tool offers an efficient strategy in genomewide association studies, even when the measurement errors associated with DNA pooling are nonnegligible. For any disease model, we find that all the statistical results essentially depend on the population allele frequency and the allele frequency differences between the cases and controls at the disease-associated markers. The general conclusions hold whether the second stage uses an entirely independent sample or includes both the samples used in the first stage and an independent set of samples.

On the sample size requirement in genetic association tests when the proportion of false positives is controlled.

With respect to the multiple-tests problem, recently an increasing amount of attention has been paid to control the false discovery rate (FDR), the positive false discovery rate (pFDR), and the proportion of false positives (PFP). The new approaches are generally believed to be more powerful than the classical Bonferroni one. This article focuses on the PFP approach. It demonstrates via examples in genetic association studies that the Bonferroni procedure can be more powerful than the PFP-control one and also shows the intrinsic connection between controlling the PFP and controlling the overall type I error rate. Since controlling the PFP does not necessarily lead to a desired power level, this article addresses the design issue and recommends the sample sizes that can attain the desired power levels when the PFP is controlled. The results in this article also provide rough guidance for the sample sizes to achieve the desired power levels when the FDR and especially the pFDR are controlled.